Acute leukemias are a heterogeneous group of diseases arising from the clonal expansion of malignant hematopoietic progenitor cells. The heterogeneity of the disease is evidenced by the large diversity of antigenic and light scatter profiles of leukemic cells in patients diagnosed with acute leukemia. This heterogeneity and a poor correlation with normal cell differentiation lead to a lack of consensus in the panel of reagents employed for classification and a lack of uniform criteria for lineage assignment. However, the antigen profiles in acute leukemias are of clinical importance as the various subgroups identified have been associated with different prognoses and serve as a guide for different treatment protocols.
Immunophenotyping by flow cytometry has significantly reduced inter-observer variations in the subclassification of leukemias and has been shown to be particularly powerful in discriminating between myeloid, B-lymphoid and T-lymphoid leukemias. However, traditional flow immunophenotyping may produce biased results due to heterogeneity in leukemias. At the present time there is a lack of consensus in the panel of reagents employed for classification and a lack of uniform criteria for lineage assignment.
Traditional flow immunophenotyping is based on finding an optimal light scatter gate followed by application of marker settings on the immunofluorescence parameters. The distribution of the cells in a display of forward and orthogonal light scatter varies considerably between leukemias, however, and does not fit the normal lymphocyte, blast, monocyte and granulocyte light scatter regions. In addition to difficulties in assessing the appropriate light scatter gate, there are complications arise when attempting to define "negative" versus "positive" immunofluorescence staining in immunophenotyping of leukemias.
In multidimensional flow cytometric analysis the bias which is introduced by employing gates on light scatter parameters is eliminated because all parameters are analyzed simultaneously. Cluster algorithms (Salzman, G. C., et al. 1991. Cytometry Suppl. 5:64), principal components analysis (Leary, J. F., et al. 1988. Cytometry Suppl. 2:99), neural nets (Frankel, D. S., et al. 1989. Cytometry 10:540) and PAINT-A-GATE analysis (U.S. Pat. No. 4,845,653) are among the approaches used for multidimensional analysis. These algorithms permit a more precise identification of cell populations in the multidimensional data space. All require listmode data files in which identical reagents are used. The number of reagents needed for most clinical applications, however, far exceeds the number of available fluorochromes and therefore requires the use of multiple reagent combinations, i.e., running a multi-tube panel with two to three reagents at a time.
The necessity for a large panel of monoclonal antibodies to achieve an optimal lineage assignment of acute leukemias forces the investigator to stain multiple samples using either one, two or three color immunofluorescence. The presence of multiple normal and leukemic cell populations in bone marrow or peripheral blood from patients with leukemia results in a variable number of identifiable cell populations in the samples stained with different antibodies. It is therefore difficult for the investigator to employ objective criteria to assess the antigenic profile of the leukemia. Although the optimal solution to the problem is to determine the antigenic profile in one tube stained with all the required monoclonal antibodies, at the present time not enough different fluorochromes are available.
The present invention employs a novel data analysis method which associates cell populations across tubes and links the positional information of these cell populations to a decision table for classification as normal cells (monocytes, neutrophils, eosinophils, basophils, NK cells, T-lymphocytes and B-lymphocytes) or as leukemic cell populations typical of B-lineage ALL acute lymphoblastic leukemia, T-lineage ALL, AML acute myeloblastic leukemia, AUL acute undifferentiated leukemia, and B-CLL B-lineage chronic lymphocytic leukemia. This approach to data analysis can be generalized to any combination of flow experiments which require data analysis across multiple tubes. The instant use for assigning lineages to acute leukemias is provided by way of example.